Gérard Noiriel, Préférence nationale. Leçon d’histoire à l’usage des contemporains
Bibliographic record
Abstract
Celles et ceux qui n’auraient pas lu tout ou partie des œuvres de l’historien auteur du Creuset français (Seuil, 1988) et, avec Stéphane Beaud, de Race et sciences sociales (Agone, 2021) bénéficieront ici d’une utile piqûre de rappel grâce à cette publication, dense, vive, synthétique. Il y est question d’histoire : celle des invariants idéologiques et des durcissements législatifs des politiques dites de « préférence nationale ». Il y est question d’actualité, avec la énième loi sur l’immig...
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Book review of a historian's essay on national preference and immigration politics; domain commentary.
This is a book review about immigration history and does not study research practice.
Book review of Noiriel on national-preference immigration history; not about research practice.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".